Dataset Information

How wait-times, social media, and surgeon demographics influence online reviews on leading review websites for joint replacement surgeons.

ABSTRACT: Objective:With the growth and popularity of the internet, physician review websites are being utilized more frequently by patients to learn about and ultimately select their provider. These sites allow patients to comment on the care they received in a public forum for others to see. With outcome and "quality" measures being used to dictate reimbursement formulas; online patient reviews may affect a physician's compensation in the near future. Therefore, it is of paramount importance for physicians to understand how best to portray themselves on social media and other internet sites. Methods:In this retrospective study, we identified 145 arthroplasty surgeons via the AAHKS database. Then, surgeon data was collected from Healthgrades (HG) and Vitals (V). We identified if the surgeon had social media (SM) accounts by using google search. The number of ratings and comments, overall rating, reported wait-times and physician SM presence were analyzed with univariate, bivariate and multivariate analyses. Results:64% of surgeons had a SM presence, and younger surgeons with SM had lower distribution of wait-times. A SM presence correlated with significantly higher frequency of total ratings and comments. Both review sites showed that younger physicians with a SM presence had increased frequency of ratings and comments and a quicker office wait-times. SM presence did not impact the overall scores on either website. Conclusion:Having SM presence is correlated with increased number of ratings and comments on physician review sites, possibly revealing an increased likelihood of these physicians encouraging their patients to engage with them via the internet. SM presence did not correlate with higher review scores, displaying that there are many complex factors that go into a physician score outside of SM and internet appearance. Future studies should explore patient comments on these sites to understand additional factors that may optimize a patient's experience.

SUBMITTER: Damodar D

PROVIDER: S-EPMC6611840 | biostudies-literature | 2019 Jul-Aug

REPOSITORIES: biostudies-literature

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How wait-times, social media, and surgeon demographics influence online reviews on leading review websites for joint replacement surgeons.

Damodar Dhanur D Donnally Chester J CJ McCormick Johnathon R JR Li Deborah J DJ Ingrasci Giuseppe V GV Roche Martin W MW Vakharia Rushabh M RM Law Tsun Y TY Hernandez Victor H VH

Journal of clinical orthopaedics and trauma 20190125 4

<h4>Objective</h4>With the growth and popularity of the internet, physician review websites are being utilized more frequently by patients to learn about and ultimately select their provider. These sites allow patients to comment on the care they received in a public forum for others to see. With outcome and "quality" measures being used to dictate reimbursement formulas; online patient reviews may affect a physician's compensation in the near future. Therefore, it is of paramount importance for ...[more]

PMID: 31316251

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Project description:Objective: Community addiction treatment agencies have utilized Network for the Improvement of Addiction Treatment (NIATx), a proven implementation strategy, to reduce appointment wait-times. However, its effectiveness at reducing medication access wait-times has not been explored. Thus, we conducted an exploratory analysis to evaluate the impact of the NIATx implementation strategies on reduced wait-times to addiction, psychotropic or both medications for individuals with co-occurring disorders (COD). Methods: In a cluster-randomized waitlist control group design, community addiction treatment agencies (n = 49) were randomized to receive the NIATx strategy (Cohort 1, n = 25) or to a Waitlist control (Cohort 2, n = 24). All agencies had a 12-month active intervention period. The primary outcome was the medication encounter wait-time. A univariate general linear model analysis utilizing a logarithmic (log10) transformation examined medication wait-times improvements. Results: The intent-to-treat analysis for psychotropic medications and both medications (reflecting integrated treatment) showed significant main effects for intervention and time, especially comparing Baseline and Year 1 to Year 2. Conversely, only the main effect for time was significant for addiction medications. Wait-time reductions in Cohort 1 agencies was delayed and occurred in the sustainment phase. Wait-times to a psychotropic, addiction, or both medications encounter declined by 3 days, 4.9 days, and 6.8 days, respectively. For Cohort 2 agencies, reduced wait-times were seen for psychotropic (3.4 days), addiction (6 days), and both medications (4.9 days) during their active implementation period. Same- or next-day medication access also improved. Conclusions: NIATx implementation strategies reduced medication encounter wait-times but timing of agency improvements varied. Despite a significant improvement, a three-week wait-time to receive integrated pharmacological interventions is clinically suboptimal for individuals with a COD in need of immediate intervention. Community addiction treatment agencies should identify barriers and implement changes to improve medication access so that their patients "wait no longer" to receive integrated treatment and medications for their COD.

Project description:ImportanceTimely access to medical care is an important determinant of health and well-being. The US Congress passed the Veterans Access, Choice, and Accountability Act in 2014 and the VA MISSION (Maintaining Systems and Strengthening Integrated Outside Networks) Act in 2018, both of which allow veterans to access care from community-based clinicians, but geographic variation in appointment wait times after the passage of these acts have not been studied.ObjectiveTo describe geographic variation in wait times experienced by veterans for primary care, mental health, and other specialties.Design, setting, and participantsThis is a cross-sectional study using data from the Veterans Health Administration (VHA) Corporate Data Warehouse. Participants include veterans who sought medical care from January 1, 2018, to June 30, 2021. Data analysis was performed from February to June 2022.ExposuresReferral to either VHA or community-based clinicians.Main outcomes and measuresTotal appointment wait times (in days) for 3 care categories: primary care, mental health, and all other specialties. VHA medical centers are organized into regions called Veterans Integrated Services Networks (VISNs); wait times were aggregated to the VISN level.ResultsThe final sample included 22 632 918 million appointments for 4 846 892 unique veterans (77.3% male; mean [SD] age, 61.6 [15.5] years). Among non-VHA appointments, mean (SD) VISN-level appointment wait times were 38.9 (8.2) days for primary care, 43.9 (9.0) days for mental health, and 41.9 (5.9) days for all other specialties. Among VHA appointments, mean (SD) VISN-level appointment wait times were 29.0 (5.5) days for primary care, 33.6 (4.6) days for mental health, and 35.4 (2.7) days for all other specialties. There was substantial geographic variation in appointment wait times. Among non-VHA appointments, VISN-level appointment wait times ranged from 25.4 to 52.4 days for primary care, from 29.3 to 65.7 days for mental health, and from 34.7 to 54.8 days for all other specialties. Among VHA appointments, wait times ranged from 22.4 to 43.4 days for primary care, from 24.7 to 42.0 days for mental health, and from 30.3 to 41.9 days for all other specialties. There was a correlation between wait times across care categories and setting (VHA vs community care).Conclusions and relevanceThis cross-sectional study found substantial variation in wait times across care type and geography, and VHA wait times in a majority of VISNs were lower than those for community-based clinicians, even after controlling for differences in specialty mix. These findings suggest that liberalized access to community care under the Veterans Access, Choice, and Accountability Act and the VA MISSION Act may not result in lower wait times within these regions.

Dataset Information

How wait-times, social media, and surgeon demographics influence online reviews on leading review websites for joint replacement surgeons.

Publications

How wait-times, social media, and surgeon demographics influence online reviews on leading review websites for joint replacement surgeons.

Similar Datasets

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